I'm working on a project where I am training a classifier on a training set of microarrays, and then I want to use the classifier to classify individual array samples independenly of each other (essentially simulating a clinical diagnostic setting, where individual samples must be analyzed separately from each other). This means that for each individual sample that I want to classify, I need to somehow normalize that array to the training set, or else the trained classifier will not be applicable. Are there any methods for normalizing a new array to an existing set of already-normalized arrays?